Shah Mirai, Ferra Gabrielle, Fitzgerald Susan, Barreira Paul J, Sabeti Pardis C, Colubri Andrés
Harvard College, Cambridge, MA, USA.
Brown University, Providence, RI, USA.
R Soc Open Sci. 2022 Jan 26;9(1):210948. doi: 10.1098/rsos.210948. eCollection 2022 Jan.
College campuses are vulnerable to infectious disease outbreaks, and there is an urgent need to develop better strategies to mitigate their size and duration, particularly as educational institutions around the world adapt to in-person instruction during the COVID-19 pandemic. Towards addressing this need, we applied a stochastic compartmental model to quantify the impact of university-level responses to contain a mumps outbreak at Harvard University in 2016. We used our model to determine which containment interventions were most effective and study alternative scenarios without and with earlier interventions. This model allows for stochastic variation in small populations, missing or unobserved case data and changes in disease transmission rates post-intervention. The results suggest that control measures implemented by the University's Health Services, including rapid isolation of suspected cases, were very effective at containing the outbreak. Without those measures, the outbreak could have been four times larger. More generally, we conclude that universities should apply (i) diagnostic protocols that address false negatives from molecular tests and (ii) strict quarantine policies to contain the spread of easily transmissible infectious diseases such as mumps among their students. This modelling approach could be applied to data from other outbreaks in college campuses and similar small population settings.
大学校园易发生传染病暴发，迫切需要制定更好的策略来减轻其规模和持续时间，尤其是在全球教育机构在新冠疫情期间适应面对面教学的情况下。为满足这一需求，我们应用了一个随机 compartmental 模型来量化2016年哈佛大学应对腮腺炎暴发时大学层面应对措施的影响。我们使用该模型来确定哪些控制干预措施最有效，并研究没有干预措施和有早期干预措施的替代情景。该模型考虑了小群体中的随机变化、缺失或未观察到的病例数据以及干预后疾病传播率的变化。结果表明，大学健康服务部门实施的控制措施，包括对疑似病例的快速隔离，在控制疫情方面非常有效。如果没有这些措施，疫情规模可能会扩大四倍。更普遍地说，我们得出结论，大学应采用（i）解决分子检测假阴性问题的诊断方案，以及（ii）严格的隔离政策，以控制腮腺炎等易传播传染病在学生中的传播。这种建模方法可应用于大学校园和类似小群体环境中其他疫情的数据。
